{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import os.path as osp\n",
    "from glob import glob\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from pylab import rcParams\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "rcParams['figure.figsize'] = 8, 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>lead</th>\n",
       "      <th>label</th>\n",
       "      <th>filename</th>\n",
       "      <th>path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>209</td>\n",
       "      <td>MLII</td>\n",
       "      <td>N</td>\n",
       "      <td>58087</td>\n",
       "      <td>/home/xperience/hse/graduate/code/data/1D/209/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111</td>\n",
       "      <td>MLII</td>\n",
       "      <td>L</td>\n",
       "      <td>523610</td>\n",
       "      <td>/home/xperience/hse/graduate/code/data/1D/111/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>213</td>\n",
       "      <td>MLII</td>\n",
       "      <td>N</td>\n",
       "      <td>104173</td>\n",
       "      <td>/home/xperience/hse/graduate/code/data/1D/213/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>115</td>\n",
       "      <td>MLII</td>\n",
       "      <td>N</td>\n",
       "      <td>597452</td>\n",
       "      <td>/home/xperience/hse/graduate/code/data/1D/115/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>105</td>\n",
       "      <td>MLII</td>\n",
       "      <td>N</td>\n",
       "      <td>126734</td>\n",
       "      <td>/home/xperience/hse/graduate/code/data/1D/105/...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  lead label  filename  \\\n",
       "0   209  MLII     N     58087   \n",
       "1   111  MLII     L    523610   \n",
       "2   213  MLII     N    104173   \n",
       "3   115  MLII     N    597452   \n",
       "4   105  MLII     N    126734   \n",
       "\n",
       "                                                path  \n",
       "0  /home/xperience/hse/graduate/code/data/1D/209/...  \n",
       "1  /home/xperience/hse/graduate/code/data/1D/111/...  \n",
       "2  /home/xperience/hse/graduate/code/data/1D/213/...  \n",
       "3  /home/xperience/hse/graduate/code/data/1D/115/...  \n",
       "4  /home/xperience/hse/graduate/code/data/1D/105/...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_json('../data/train.json')\n",
    "val = pd.read_json('../data/val.json')\n",
    "mapping = json.load(open('../data/class-mapper.json'))\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f300b6cd0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['label'], order = train['label'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f2c8d9710>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(val['label'], order = val['label'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N    0.001055\n",
       "L    0.009770\n",
       "R    0.010870\n",
       "V    0.011075\n",
       "\\    0.021793\n",
       "A    0.030991\n",
       "!    0.167062\n",
       "E    0.747383\n",
       "Name: label, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 1 / train['label'].value_counts()\n",
    "a = a / sum(a)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'N': 0, 'L': 1, 'V': 2, '\\\\': 3, 'R': 4, 'A': 5, '!': 6, 'E': 7}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "weights = np.zeros(len(mapping))\n",
    "for i in a.index:\n",
    "    weights[mapping[i]] = a[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00105497, 0.00977039, 0.01107494, 0.02179295, 0.01086978,\n",
       "       0.03099145, 0.16706216, 0.74738336])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import wfdb\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ecg_data = sorted([osp.splitext(i)[0] for i in glob('../mit-bih/*.atr')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ecg = ecg_data[0]\n",
    "ann = wfdb.rdann(ecg, extension='atr')\n",
    "record = wfdb.rdrecord(ecg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7f2c78a7d0>,\n",
       " <matplotlib.lines.Line2D at 0x7f7f2c78af50>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(record.p_signal[0:256])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NpDqtWT3XyJkre7fhrRv6sG7fST5bHsdML0MUlappfHW9FkdE1hpjIotdriYkhPIYl1wRaoUIuWXohnLnvrGU5+dt36yux1DC8jDjwUGs3pPACzO8X+RTShUW9fQwWjSoXap1/U0INa7LqCopr2QA+UlgxoODyq1OoNyTAeAxrFMp5Z+bPl7JvAoecl7jLipXd7qzVap6OpTo/bYg5UkTglJKBYPiR5qXmSYEpZRSgCYEpZQKCpVwgqAJQSmlgoEUvENgBdCEoJRSCtCEoJRSQaESThA0ISillLJoQlBKqSCgF5WVUkoBelFZKaWUTc8QlFJKAXpRWSmlVCXShKCUUkFBryEopZSqJD4Tgoj8W0TaVVYwSimlvKsK1xDaACtEZJmI3CciLcuzcREZLiLbRSRWRMaUZ91KKVWdBHyUkTHmUeA04GngLGCjiMwRkdtFpGFZGhaRUOA9YATQHbhRRLqXpU6llKquqsIZAsay1BjzL6Ad8F/gEeBIGdvuD8QaY3YbY7KB74CrylinUkpVS1KVLiqLyFnAC1hH9VnA2DK23RbY7/Y+3i4r2O5oEYkSkahjx46VsUmllFJFqeVrpoh0BW6wXw6so/hLjTG7KyE2AIwxHwEfAURGRpbfU+mVUkp58JkQgDnAVOAfxpjN5dz2AaC92/t2dplSSqkCKuMags+EYIzp7P5eRBq5r2OMSShD22uAriLSESsR3ADcVIb6lFJKlUFxZwgAiMj/Ac8DmYCz28YAnUrbsDEmV0QeAOYCocBnxpgtpa1PKaVU2fiVEIAngJ7GmOPl2bgxZhYwqzzrVEqp6igrN6/C2/B3lNEuIL0iA1FKKVW0hLTsCm/D3zOEscBfIrIKa8gpAMaYhyokKqWUUpXO34QwGVgEbAIq/rxFKaVUpfM3IYQZYx6r0EiUUkoFlL/XEGbbvxg+VUSaOV8VGplSSqlK5e8Zwo32v+63qyjTsFOllFJVS3HPQ2gDYIzp6OUVNMngoYu7BjoEVUW9d1M/5jwymBD7V6Bv3dAnsAEF0J4JI/36rix78qJKiEYV1Kx+eIW3UVyX0ScislJEJorIhSLi7xlFlXJORFMAWjeqTcyLw30uGzdxFCN6nlKqdjq1qM+0+8+nf0QzbjinPe2a1i1xHQ1r1yJu4ijev7kf797Ul38O7uiaN+Hqs4h5cThT7jmXV685i9iXR1AvPNSveuMmjmL2w4Nd7//ety3R4y9lSDf/HnHxwlU9CLX3mrVrWZvNuR2bMeHqs/z9aF4N7tqiyHnDzmzlml497mLuv8j64fx//9Hbr7r3TBjJpOsLL/vXmKHETRzFirFDGdXrVM44pRG7XhnJyrEXc1Wftux8eYTX+q7o3YYFj13Ad6MHuMou6NaS+uGhtGhQm6FntGL+o0O4/6LORD09jL6nNfErTqedL49gz4SRfH7HOXx+5zn8NWYoH916Ns9f2YO4iaP48JZ+3DOoY/EVebH9Jc/tvmfbRjx7hefd5kWExy7pxtR/DqCgf192OgDzHh1C+2b1iJs4ivGX56/ftVUD4iaO4pLurQE4v0vzImNpULtsu5F1z1zCJ7dF+lymNN+9ivTZHfnxfnlX/1LV8dTIM8srnCKJMb7vFycidYALsZ5bcD6wD+seR3OMMfsqOkB3kZGRJioqqsTr5eUZ3lkUy20DO9C0fjgRY2Z6zL9tYAemRx/k3gs6c+8F1k5n9Z4Erp+8gp5tG7H5QDJzHhnMGac04kRqFt9H7Wfj/iTmbDkMQN2wUL68qz/9O3peVtl2KJknfoymXngop5/SkJf+dhZJ6Tn846MVxBxOKRTnJd1b87GXDX3WpkNENK9P9zaNvH4+5+dpWLsWtw7swPtLdgHWzvb6yPYMPaMV9e0v4bwth9lyMJlHL+nmWn/t3gSOpWTRo01jBr+22KPuOY8Mpn54Ldo3q4cxhveX7OK6yHYkpucQ0bw+4bVCWL7zOBEt6jHo1cWECOTZm9Sr15zFloPJ3DYwgt+jD/LWwp08fHFXVuw+weo9CQzs1Jypowew53gab8zbzr8u6Mzl7ywHYONzl1IvLJQu42bTtF4Y68df6hHXJ8t289LMbfxy33m0aVyXh79bzzOXd6dJvTBu/mQVX93Vnw7N6wNwKCmD2rVCycxxUCcs1O8jrenRB3lo6noA1owbRsuGtV3z0rJyCRGhro+EfDgpk8vfWc7xVGukdu92jYmOT+LeCzrze/RBDiRmAPDzvwbSuG44XVo18CsugO2HU7jsf38AMPWfAzirXWMa1K6FMYaOY2dx3dntuPbsdvzjo5XMfGgQPdo0ZueRFEa+vYwch2HTc5fSsE4Y2w4l06F5PfJM4R31ziMp/LzuALcMOI12TesVisEYw9ZDyfRo09hVlpnjYPWeBIZ0a8mhpAwGTlhE73aNGTPiTAZ2zk8Sf+06zuKYo7RuVIeXZm4DYPbDgxnx1jIAerRpxC0DOjB9w0G+urs/XcfNBqwDG3dzNh9mwbYjtGpYm66tG3DR6a1oUs/z/9f9+/7tPedy8yervP9NXxrO3C1HXP/n9cNDSct2FFpuUJcWLI/1/RvdN6/rzeM/RgO4/tbuDiZmcOfna7jojFZ8uNT6vp7VtjETrj6L6PhExv26mRkPDiLHkcff3/8LsBLJBX4ewBUkImuNMb6zKH4kBC8Vd8RKDsOBU4wxpUt3pVDahFBQalYua+IS6NCsHp8s38OLV/V0Hf0WXK5uWKjXeWB9YUSkRF9kp1xHHl3GzWbKP8/lvM5FHyX7w7nBx7w4nDphoa73b1zXm2vPLt0TUE+mZZObZzx2gsXJyHYQEgJJGTnUDg2lcb38L4Ejz7By9wnO79KC9OxcPlu+h3sv6Eyt0JBCdYSGCOG1Av+4b2MMny7fQ6tGdbiyd5tyrz/2aCqdW9ZHyvmuZcVtt1VNr+fmkpyZS9zEUVz13p9E709k+X8u8khCHcfO5Jp+7XjjOv/ODt05vw/OZHL1+3+ybl8iv9x3HgcTMzi1cV06tqhf5IHC79EHedBOEgAzHhxEnjFc+e6fHstNuedcEOjbvil1w0MLteuNMYar3vuTjfFJbHn+MteBm7u1e09yzQd/Vc2EUKCRcPvhNpWivBJCdbM/IZ2QEKFtE+s0eeLsGD5cuouv7+7P4K7l+tRTpcpdriMPhzHUruVf92dJFdwxZ2Q7SM7MoXWjOiWuw70egPTsXEJDhBOp2bRpUtfrOr4SAkB2bh7HUrNc39+CKjMhFPc8hBTyb2YH1mM9jfNfY4z3PgxVqdo38zydf+ySbvRp30STgQoKtUJD/B7uWB7qhof67OrzJfpZz67LeuFW5AWTAcCfY4biz0laeK2QIpNBZSvu9tdlem6yCozwWiEML+WFcaVU0RrXDSt+IVtV2cmXRHFnCD5/fFbG5yEopVRQ+O3+89l/svrf37O4M7W15HcRFaQ/TFNK1Qi92zehd/uSDSMORsV1GRU56FlE2pZ/OEoppQKlLGP7VpRbFEoppQKuLAkhOAY5K6WU8ktZEkLpf8CglFKqyilulNE7eN/xC1D9r7AopVQNUtwoI18/C9afDCulVDVS3CijLysrEKWUUoFVXJfRdF/zjTFXlm84Siml3HVoXo/XrulFt9Ylv4lmSRXXZTQQ2A9MBVahI4uUUqpStWhQm+vPaV8pbRWXEE4BLsF6hOZNwExgqjFmS0UHppRSqnL5HHZqjHEYY+YYY24HBgCxwBIReaBSolNKKVVpir3rrIjUBkZhnSVEAG8Dv1ZsWEoppSpbcReVvwJ6ArOA540xmyslKqWUUpWuuDOEW4A04GHgIbdH/ekDcpRSqpop7ncIgX+wrVJKqUqhO3yllFKAJgSllFI2TQhKKaUATQhKKaVsAUkIIvK6iMSIyEYR+VVE9FbaSikVYIE6Q5gP9DTG9AJ2AGMDFIdSSilbQBKCMWaeMSbXfrsSaBeIOJRSSuWrCtcQ7gJmFzVTREaLSJSIRB07dqwSw1JKqZql2HsZlZaILMC6W2pB44wxv9nLjANygW+LqscY8xHwEUBkZKQ+x1kppSpIhSUEY8wwX/NF5A7gcuBiY4zu6JVSKsAqLCH4IiLDgSeBC4wx6YGIQSmllKdAXUN4F2gIzBeRDSLyYYDiUEopZQvIGYIxpksg2lVKKVW0qjDKSCmlVBWgCUEppRSgCUEppZRNE4JSSilAE4JSSimbJgSllFKAJgSllFI2TQhKKaUATQhKKaVsmhCUUtVaiwbhgQ4haATk1hVKKVVZZj00mD3H0wIdRlDQhKCUqtZaNapDq0Z1Ah1GUNAuI6WUUoAmBKWUUjYJpoeVicgxYG8pV28BHC/HcCqTxh4YwRp7sMYNGntF6WCMaVncQkGVEMpCRKKMMZGBjqM0NPbACNbYgzVu0NgDTbuMlFJKAZoQlFJK2WpSQvgo0AGUgcYeGMEae7DGDRp7QNWYawhKKaV8q0lnCEoppXzQhKCUUgqoIQlBRIaLyHYRiRWRMZXY7mciclRENruVNROR+SKy0/63qV0uIvK2HeNGEennts7t9vI7ReR2t/KzRWSTvc7bIiK+2ihh7O1FZLGIbBWRLSLycLDELyJ1RGS1iETbsT9vl3cUkVV2e9+LSLhdXtt+H2vPj3Cra6xdvl1ELnMr97pNFdVGCeMPFZH1IjIjyOKOs/8/N4hIlF1W5bcXu44mIvKTiMSIyDYRGRgssZcrY0y1fgGhwC6gExAORAPdK6ntIUA/YLNb2WvAGHt6DPCqPT0SmA0IMABYZZc3A3bb/za1p5va81bby4q97ghfbZQw9lOBfvZ0Q2AH0D0Y4rfra2BPhwGr7HZ+AG6wyz8E/mVP3wd8aE/fAHxvT3e3t5faQEd7Owr1tU0V1UYJ438MmALM8FVnFYw7DmhRoKzKby/2el8C99jT4UCTYIm9PF8Ba7jSPiAMBOa6vR8LjK3E9iPwTAjbgVPt6VOB7fb0ZODGgssBNwKT3con22WnAjFu5a7limqjjJ/jN+CSYIsfqAesA87F+hVprYLbBTAXGGhP17KXk4LbinO5orYpex2vbZQg3nbAQmAoMMNXnVUpbnu9OAonhCq/vQCNgT3Yg2yCKfbyftWELqO2wH639/F2WaC0NsYcsqcPA63t6aLi9FUe76XcVxulYndF9MU60g6K+O1ulw3AUWA+1pFxojEm10t7rhjt+UlA81J8puY+2vDX/4AngTz7va86q1LcAAaYJyJrRWS0XRYM20tH4Bjwud1V94mI1A+S2MtVTUgIVZaxDgsqdNxvWdsQkQbAz8Ajxpjk8qzbH6VtwxjjMMb0wTri7g+cUd6xlTcRuRw4aoxZG+hYSmmQMaYfMAK4X0SGuM+swttLLayu3Q+MMX2BNKzum7LWWyKV0UZxakJCOAC0d3vfzi4LlCMiciqA/e9Ru7yoOH2Vt/NS7quNEhGRMKxk8K0x5pdgix/AGJMILMbqBmkiIs5ngLi354rRnt8YOFGKz3TCRxv+OB+4UkTigO+wuo3eCoK4ATDGHLD/PQr8ipWIg2F7iQfijTGr7Pc/YSWIYIi9XNWEhLAG6GqPogjHuvg2PYDxTAecow9ux+qbd5bfZo9gGAAk2aeSc4FLRaSpPQLhUqz+3UNAsogMsEcs3FagLm9t+M2u81NgmzFmUjDFLyItRaSJPV0X69rHNqzEcG0RsTvbuxZYZB+tTQduEGs0T0egK9bFQa/blL1OUW0Uyxgz1hjTzhgTYde5yBhzc1WPG0BE6otIQ+c01v/zZoJgezHGHAb2i8jpdtHFwNZgiL3cBfICRmW9sEYF7MDqRx5Xie1OBQ4BOVhHIXdj9dcuBHYCC4Bm9rICvGfHuAmIdKvnLiDWft3pVh6J9aXbBbxL/i/PvbZRwtgHYZ2+bgQ22K+RwRA/0AtYb8e+GRhvl3fC2jHGAj8Cte3yOvb7WHt+J7e6xtnxbcceGeJrmyqqjVL8/S8kf5RRlY/bXj/afm1x1h0M24tdRx8gyt5mpmGNEgqK2MvzpbeuUEopBdSMLiOllFJ+0ISglFIK0ISglFLKVqv4RaqOFi1amIiIiECHoZRSQWXt2rXHjR/PVA6qhBAREUFUVFSgw1BKqaAiInv9WU77e3boAAAgAElEQVS7jJRSSgGaEKqtrFwHe46nBToMpVQQ0YRQTY39ZRMXvbGEpIycQIeiqpETqVmBDqFG2Xcinc0HkiqtPU0I1dSfsccByMh2BDgSVV3M3XKYs19awMrdJ/xeZ/WeBOJPpvu9/JaDSWTm6DbrNOT1xVz+zvJKa08TglLKL2v2JACwKd7/I9brJ69g0KuLycp1EDFmJj9E7S9y2ZNp2Yx6ezlP/Bhdorjy8gy5jjyPstFfRflsqyj7E9LxdveG+JPp5OWV/K4OjjyDoxTrBYomhGrOBPZuuqqKSEzPZnr0wULlszYdYtTby7zuBP2VlJFDxJiZrvpzHHlk5Xoe5Z9Ms7ou35y3vch6UrOsxzGs35dYovbv/GINXcbN9iibt/UIT/60sUT1rN93ksGvLWbK6n0e5bFHUxn06mI+WLrLozwlM4dJ87Z7JKO8PONx7a7P8/M4+6X5PP5DNCPeWlaieAJBE4Kq8nYdSyVizMwSdVVUR3M2H2JRzJFSrXvFu8t5aOp65m05zPKdx13lD01dz5aDyeSW4Cg2NSuXb1ftJT07l6xcB3H2DvCTZbsBGPnWMk5/eg4f/ZG/Ax02aSlgJY8Js7eR68gjKT3/+lZGtoPsAkf5/lq641ip1nO3PyGd9xbHAjBvyxHW7Tvpmufs8lplnyE5vTonhrcXxTJj4yFX2TuLYrnojSXEHk0BICUrl8T0HH5eF8+2Qx6PE6mSNCFUU0eSrYt/C7cF9Pbq5WLFLisReDvCLYkcRx4RY2bS/+UFxR4RJ6ZnEzFmJo//ULLui5Jw5Bn+2HGMaz/4ix1HUgrNP5Ga5doZ/bw2nnu/WcddX5Tudzj7EzIAGP31Wm75dFWZdk5vLdzJuF830338XE5/eo7rHHRjfBInUrPYeTQVgFdmxbjWcR79Z+bkMXnpbp6etpneL8xjxkbr//TM8XO4+M2lruVjj6YQc7joGCfN30HEmJlc/f6frrJzXl7A0DeWsPVg/nrOC7LGGCYv3cWBxIxCdT0wZR2DX1vMAvu7snTHMa5+/y/iT6YzPfogd3y+BoBd9udyysi2EliOWyJbHWdtq1sOeo/97BfnM9k+05i2/gD7E/y/vlIZNCFUc2/4OEUPNmW9Me9vG6ydz9GULNbvT2RjfCJJ6Tn8si6e95fEur7Yl0xaypXvWjuan9fFk5zpOVIrx5HH+N82czQl09WvnJKZw+Slu4g9msrPa+MpzuGkTN5asIPbPltN1N6TjP9tM79tOODRT332Swtc/e+Pu/Wrf7UijogxM8s0+qSo7otDSfk7zLV7E9hxJIUf1uznp7XxfLJ8j9d1PlgS6xGzP75bY/XvPzBlfaGBDyIwbNIfDP/fMs59ZYEraQBk5jh4f0ksby/cCcA6t+6lYylZ7D6exsi38z/b5e8s57cNB+g4dhYTZsdw/sRF5DryePa3zRxJzgTwOMJ3N+jVxTw0db3r/YHEDLJz/TuLefi7DV6HfZ9Iy2bC7Bge/m49j3y/gcGvLWZxzFEGTlhIxJiZ/Lw2HmMMmTmOgIzoCqpfKquSS0wP/mGnISL2VP7OcvnO45zWrB6nNa/nVx2xR1M8LlbuPJLCf37e5LHMsh3HefP63q4jXKetB5MZ0Km56/3imKN8tWIvX62wfvy54LEhPDh1A9sOJTNhtnVUfM3Z7SjKoaQMBk5Y5FG2cncCK3cnkJyZy60DOvDLuvykcvrTczyWHf/bFgDeWbSTybdGem3jk2W7Xd0eszYd9rrMpf9d6uoquuKd5Vzdry2vzIrhrLaN6dqqAb+s9++haXO3lK4by+nM8Z6fL/5kflI6kpzFA1PWc3mvNoD1md9b7NmXX5yHv9vg8d55veHLFXs5v0tzb6sUKc/tqMR5fW7t3pO8uziWSdf34c/Y/G7Ni95YUmQ9zoMTsK6BOD3+YzRhtUKYumofKwLQRVqjE0JaVi51w0IJCZHiFy6j9Oxc6tQquq1jKVmEhQpN6oUXW1dKZg7/+Xkjvds14eYBHWhQu/L+GzNzHISGCGGhISzcdoSurRp67JTTsnL590/RPHdlD1o1rFMubTrzQWqWgxkbD/LOwli2210scRNH+VVHWpbnUWjBZACwYvcJzpu4qFD5DR+t5Kd7BxIZ0Qwo3B0wbNIfhdaZaR917k1I474LuwDWBccbPl7J6gJ90e6embaZy7q35jE/uqrmbjlCxJiZnHFKQ2Y/PBix/1BTV+/jpZnbil1/x5H8xBdzOMXVxbPpQBKbKnHsuz9yHXnsS0gvcTIojvsO3B/i9vX9ZZ2VMJ1nO9d88Fe5xOR+VuKUnJlDozph5VK/LzW2y2j1ngR6PDuXTk/N4uuV1pFe7NFU1rtdTHI6f+IiIsbM5K/Y44Xm+SMzx0H38XN5eZb1JV0Uc6RQn/E5Ly+gzwvziT2aSq4jj+2HUzz6QjOyHa7x2VNX72PWpsNMmB3D079u8rg4V9HOeGYOI99axtGUTO7+Moqhby7xmP/bhoPM2nSYSfN2lFubh5OsU/vfow/y6PcbXMnAafnO46yJs3ayC7cdYXr0QV6asbXc2ge49sMVvLtop9XVY3dX+HL/lHXcP2Udr83J77J7a+FOn8nAqf8rC0sUW8zhFDqOncXmA0lEjJnJ2F8KJ7tg12XcbIa6XWMIFEHYejC50n8r8ax9VljRaswZwsPfrScrJ4/3bu7HPyavIGpv/o7/mWmbuXVAB9dIiNkPDyYtK5fmDWrTvEG460LUTZ+sYu4jQzj9lIYlajvd7iP9ZV08zRuEu3YScRNHMWXVPlLc+qiHTVrKPyLb8709hvq1a3px/TntOXP8HMJDQ1j11MUeF+umbTjItA0H2fjcpZVyBAGw82gqd9mnuQVHpziPoMrzQXzp2bmu6RyHZ8XxJ9O55VPr2ejf3H0ud3+Zf9H16cu7u6b3l+DHUUV5Y94O3ihFoos7nkbDOrX8SiRlUZk/YKqpflobz1O/Vn7CLcmP+8qixiQEZ59dVFyCRzJw+rvbaAX3C25/79vWY7nL/vcHcx8ZwimN69C4rn874CXbrdELJ9NzPI4Y9xxP87pxzdua3+f75M8b2WkPYct25HHle96/9PtOpNOzbeMiY8hx5PHc9C2s25fIo8O6cmmPU/yKvSjOo/aCnGfUx1OzePi79exLSOehi7ty0emtiqwrKT2Hf3y0gvdu7kedsFDaNqnrmvfr+vgiL/qBdeHPyZkYCoo5nMwDUwqfhleWUW8v4+IzWwesfVV+ApEMAK/7rIpQIxKC+yiGaRu8Xygr6scwf3gZ43zZ/6w+4+E9TmHOlsMM7NScqaMHeF0/15FXZH9wURedThboAvp4Wf7oDufwwYLmbTlMeraDXu0aUycstND8hduO8O0q6wc3o79e67PvfX9COqc0rkNYqGePovuoieOp2a7pQ0kZfLhkFzcP6EBKpnU0vzAmf7jrnZ+vIW7iKLYfTiE5M4dz7L54pwXbjhBzOMU17LBOWAh/PHkRrRrW4dHvSz/s85lpm/l7v7Zc/X759O2WVlq2o8xDZlXNVp5n3L7UiGsI77sNi5u6umQ/Zz+Rll3kvDlbrCN552iA3zYcYPRXUazda/UTp2XllugHP2UxZfV+120CnGO+3WUU6PNMzcrlu9X7Co3HP5GaxeDXFvPI954jM6Doi2YDJyziyxV7ufPzNa7rJAVNmredy/73B9d9uKLQvJPpnn/jzJw8XppR/EXR4ny9cm/Ak4FSwaRGnCEU/IVhRXhvcSyvz7W6g+ZtPcK9F3Tmw6XlOyLCl+P2mOXjqVn0fHaux7xu42bTunFtjzLnMp1aNqB/x/wjdufdUWduPMR7N+Uvn52bR4KP5Ah4/dGP09uL8pNyxJiZPDqsG73aN+aJH6K9Jt3p0Qd56e89fbanVE2yJi6h0Nl1eZOy3MOkskVGRprSPDGt81OzguoGU5XN2X1kjKHj2FmFygGufv9Pjx8BKaUq14392zPh6l6lWldE1hpjvP9oxU2NOEPQZOBbxJiZxS6jyUCpwMrMKd29nkqiRlxDUKWT68jDGMNjXq4nKKUqV8E7yFaEGnGGoErn0R+iOZKUyeq4ir8Go5TybXFM2e/qWhxNCKpIv+tQSaWqjMp4tol2GSmlVBCojPE/mhCUUioIVMbQGE0ISimlAE0ISikVHLTLSCmlFOhFZaWUUja9qKyUUqrSBDQhiMhwEdkuIrEiMiaQsSilVFVWrUcZiUgo8B4wAugO3Cgi3X2vpZRSNVNl3Ig0kGcI/YFYY8xuY0w28B1wVQDjUUqpKqtanyEAbQH3p9XE22UeRGS0iESJSNSxYxV/Lw+llKqK9KIyYIz5yBgTaYyJbNmyZaDDUUqpgBjUpUWFt1FkQhCRrSLytIh0rqC2DwDt3d63s8uUUkoVIFLxbfg6Q7gRqA/ME5HVIvKoiLQpx7bXAF1FpKOIhAM3ANPLsX6llKo26oWHVngbRSYEY0y0MWasMaYz8BBwGrBSRBaLyD/L2rAxJhd4AJgLbAN+MMZsKWu9SilVHZ1xSqMKb8Ov5yEYY1ZiJYPfgP8C7wIfl7VxY8wsYFaxCyqllKpwxSYEETkHq/voGmAPMBn4sYLjUkop5SYlM7fC2/B1UfkVEdkFvI91sfd8Y8yFxpgPjTEnKjwypZRSLt+s3Fvhbfg6Q8gEhhtjdlZ4FEoppXwKr1XxvxIoMiEYY14A1y0mRgER7ssbYyZVdHBKKVVVtWlch4NJmV7n1QkLITMnz/W+YZ1aZe7yaVw3rEzr+8OflPM7cAfQHGjo9lIqaAzs1LzS23zxbz1d0xee3pLfHxhUaJnzOld+XEVpWNuvMSZ+eXL46ax+6mJmPDiIuImjeO6K6nGbsgGdmgHQpF4Yf429uMjl2jSu65q+ZcBp/Hrf+VzVp2yj9muHVfwZgj8ttDPGXG2MedYY87zzVeGRlaPLerSutLYu6d6ai89oVSF1P3N5dz67I9LnMpXxa8aifH13/2KX6dC8XpE7wbiJo1j9lPcvWccW9V3TZ57qOfzupnNP48re1pet32lNvK7/rws7EzdxFHETR/HqNWdx1/kdefFvPYmbOKrIWFeMHeqa/pufX+bvRw8gevyl/P7AIG459zRev7YXb17Xm7dv7MtZ7RozZsQZdGpRn7ZNrB3GF3f2Z0TPU1zrD+5q/f89MqwrHZrXc5UPLbBNfXBzP7/iAWvntfuVkQBEuNXp7s3rerPp+cs8tp/wUGv30Pe0Jgw7M7/9b+4+1zV97dntvNZ3U//TaNWoDj3bNgagTZP8HWSrhrUBGN7jFLq2auD356hIP907kPsvsn6D+7c+bbiit/f/72ev6MHMhwax+PELAZhyj/W3ePemvky+9WwuOr0l40aeSWe3z/XsFT3o0qoBb93Q1/X/DnBF7zYe299DQ7v4jPGi0ytmv+JOiruDnoi8Ciw0xsyr8GiKERkZaaKiokq8XkpmDn/GnmBAp2b0eWF+ofm1a4WQlZvnZc183Vo3YNL1fbj8neWF5j12STe2H0nhwm4tuS6yPZk5Ds54Zk6h5bY8fxmHkjIAGDbpD1f545d04835OwB4+8a+PDR1vcd6pzWrx3ejB7i+VInp2a7P8fSoM3lp5jbXsnETRxExZqbPzwLQuWV9dh1Lc70f2Kk5o3qdytPTNhdadvSQTnzxZxzZjjyPDbhgO3ETR3Hrp6tYtvO4R/kLV/Ug12H44q84Fj9xISECS7Yf4+yIpsQeTeXq9//i3Zv6cnkv60sYfzKdT5fvYdzIM7n0f39wSffWPDi0Kz2fnQtAn/ZNeOO63lzxznKinh5G/dq1GPvLRqau3s/Lf+/Jzed2IC/PMD36IMt2HufndfHMeHCQa+dU0G8bDvDT2njaNqnLd2v288BFXXj0km6Ehgivz43hvcW72PL8ZczZfJjHf4xm7iNDeGfRTmZsPARYR8OJ6Tk8NfLMYv/uTnl51vcuJEQwxmCMNe2NI8+Qleug+/i5Hn/rH9bsp12zurRrUo8hry8GYM24YaRk5tCobhhbDyZz22erufD0lnxxZ3+WbD9KjzaNadmwtsf/nfv/aWaOg51HUmneIJw2TeqSl2dcv5Cdu+Uw53VpQaM6Yew8kkJatoNOLevT67l5vHZtL67p147bP1vN8tjj7H5lpMfnMcb6/xjR81TCa4VwNCWTpvXCWbr9GPd8ZX2n90wYyd4T6Ww5mEzjumHc8umqYv+Ob93Qh8Z1w7jj8zV8P3oAHVvUJyfPkJCaTdfWDYg/mcGwSUuLrSd6/KU0qluLPAOhdtwzNx6iQ/N6Ht95XwcQ7hbFHOGuL6IKrZPryKPLuNkATL71bC7rcQrT1h/gke83sOjxC2hWP5yw0BCe/30Lu46l8fO/znPNHzfyTP45pJNf7RckImuNMb6PJvEvIfwd+AbrbCIHEMAYYyr+VxIFlDYhuHtm2mYiWtTn7kEdWbn7BAKc26k5mw8kcTQlk7DQEG79dLXHOpf1aM3Eq3vRtH44ALuPpXLNB39xMj2HcyKa8uO953ltKyvXwQ9r9tOkXjgtGtRmYIEj47OencvkW8/mPLejsv0J6Qx+bbHrfVEb4GfL9/DCjK0se/KiQsu7f9mn3HMuK3ef4O7Bnej9fH5OXzNuGOe8vMCjjcT0bCJfWsC4UWfy/O9bOeOUhsx5ZEiRf8tDSRls2JfIRWe0Iis3z9XHmZSRQ52wEKatP8B/ft7EvEeH0K112XsZnZ9r+X8uol1TzyPdA4kZPPb9Bj66NZLG9fL7WjOyHfwZe5xh3Ys/S3xvcSyvz93OY5d046GLu5Y53vKWkJaNACfTs+nUsvgja0eeYcKsbYwe0olWjep4zLt/yjraNa3LIxd3o245/gI2LSuXg4kZdPXz/zsvz/D5X3HccE576hfosiruwGbL85cVWscbZz1RTw/jgtcWk5btKLSMrx39un0nufr9v4pdrqC+L8zjit5teOGqnh7lznj2TBiJ+HE/CmMMszcf5rIep7iSVUmVZ0LYg3Vb6k2mMm7I7UN5JAR/7DqWSl6eYdexVIb3PLXC23OXnJlDr+esHfeXd/Xngm7eb+hnjCE5I5fG9cIKHe0VdfTnnmziJo6i7wvzOJmeU2gjz3Xk8fD3G7j/wi50b1P6vG+MISkjhyb1wktdh7uMbAeHkzM9uo/KU0a2g0nzt/PYJaeX605Slc7qPQlcP3kFX9x5Dnd8vqbQfH93zs7vQ9zEUSSmZ5OR4+D/vl7LxvgkAPpHNOOHewf6XUdZlWdd/vI3IfhzFWk/sDnQyaAydbaPvvw9yilPjeqE8cHN/ejXoSmtCxzVuRMRjyNhf7RvVo8BnZqR67D+K+c+OoSjyVmFlqsVGsJ7N/nfR+0rxvJKBgB1w0MrLBk46x83qnpc/KwO+nds5joLeOLSbrwxb0eZ62xSL5wmwHT7Av+BxAyaleM26q8uVeTaSUH+JITdwBIRmQ249h467LTijDirdGclpzXzfsHQ3Xej84+EWjWsQ6uGRScdpQLN2SX0wNCuDO95KifTs7nuwxXlVr/7Rd7KEvvyCL+6igLBn4Swx36F2y9VRRUciaJUdeI8qn7hqh50alE1j7D9USu06j6GpsiEICJjgTnBNsS0Jhp/eXdemLE10GEoVSluGxhR6W2OG3kmjhrQa+7rDGE38LCI9AaigdnAPGPMyUqJTPntush2rNh9gvsv8j2OWSlVOqUd7hlsfN264nvgewAR6QsMB36xb2WxAOvsYXVR66vK07BOGB/fVuwAAqWU8smvzixjzHpjzARjzEXA5cAW4J4KjUyVWpMSjj5SSinw8wE5ACLSEegLbDXG/Az8XGFRqTLJyvH9q2ullPLG1/MQprlNXwUsAq4AfhOROyo+NFVahup/8UspVf58nSF0cJv+DzDUGLNHRFoAC4EvKjIwVXqhVXSMs1KqavN1DcH9MLOWMWYPgDHmOKB9ElVYjkPPEJRSJecrIfQSkWQRSQH6isipACISDuiNXqqwYd31B2pKqZLzlRA+AkYaYxoaY8KMMYfs8nrA/1V8aKq0nPeo79m20m9Iq5QKYr4SwnbgdRGJE5HX7N8iYIxJNMaU381EVLlz2B16rfU+RUqpEigyIRhj3jLGDAQuAE4An4lIjIg8KyLdKi1CVWK1Qq2Lyg3rlN8jEZVS1V+xP0wzxuw1xrxqjOkL3Aj8DdhWzGoqgC7o2pInLu3G8wUezKGUUr4UmxBEpJaIXCEi32Ldz2g7cHWFR6ZKLSREeGBoV9cTzJRSyh++7nZ6CdYZwUhgNfAdMNoYk1bUOkoppYKXr07mscAU4HG9w6lSSlV/vu52OrQyA1FKKRVYVffRPUoppSqVJgSllFKAJgSllFI2TQhKKaUATQhKKaVsmhCUUkoBmhCUUkrZNCEopZQCNCEopZSyaUJQSikFaEJQSillC0hCEJHX7YftbBSRX0WkSSDiUEoplS9QZwjzgZ7GmF7ADqw7qyqllAqggCQEY8w8Y0yu/XYl0C4QcSillMpXFa4h3IX1JDavRGS0iESJSNSxY8cqMSyllKpZKuwp7CKyADjFy6xxxpjf7GXGAbnAt0XVY4z5CPgIIDIy0lRAqEoppajAhGCMGeZrvojcAVwOXGyM0R29UkoFWIUlBF9EZDjwJHCBMSY9EDEopZTyFKhrCO8CDYH5IrJBRD4MUBxKKaVsATlDMMZ0CUS7SimlilYVRhkppZSqAjQhKKWUAjQhKKWUsmlCUEopBWhCUEopZdOEoJSq1u44LyLQIQQNCaYfCUdGRpqoqKhAh6GUUkFFRNYaYyKLW07PEJRSSgGaEJRSStk0ISillAKC7BqCiBwD9pZy9RbA8XIMpzJp7IERrLEHa9ygsVeUDsaYlsUtFFQJoSxEJMqfiypVkcYeGMEae7DGDRp7oGmXkVJKKUATglJKKVtNSggfBTqAMtDYAyNYYw/WuEFjD6gacw1BKaWUbzXpDEEppZQPmhCUUkoBNSQhiMhwEdkuIrEiMqYS2/1MRI6KyGa3smYiMl9Edtr/NrXLRUTetmPcKCL93Na53V5+p4jc7lZ+tohsstd5W0TEVxsljL29iCwWka0iskVEHg6W+EWkjoisFpFoO/bn7fKOIrLKbu97EQm3y2vb72Pt+RFudY21y7eLyGVu5V63qaLaKGH8oSKyXkRmBFnccfb/5wYRibLLqvz2YtfRRER+EpEYEdkmIgODJfZyZYyp1i8gFNgFdALCgWigeyW1PQToB2x2K3sNGGNPjwFetadHArMBAQYAq+zyZsBu+9+m9nRTe95qe1mx1x3hq40Sxn4q0M+ebgjsALoHQ/x2fQ3s6TBgld3OD8ANdvmHwL/s6fuAD+3pG4Dv7enu9vZSG+hob0ehvrapotooYfyPAVOAGb7qrIJxxwEtCpRV+e3FXu9L4B57OhxoEiyxl+crYA1X2geEgcBct/djgbGV2H4EnglhO3CqPX0qsN2engzcWHA54EZgslv5ZLvsVCDGrdy1XFFtlPFz/AZcEmzxA/WAdcC5WL8irVVwuwDmAgPt6Vr2clJwW3EuV9Q2Za/jtY0SxNsOWAgMBWb4qrMqxW2vF0fhhFDltxegMbAHe5BNMMVe3q+a0GXUFtjv9j7eLguU1saYQ/b0YaC1PV1UnL7K472U+2qjVOyuiL5YR9pBEb/d7bIBOArMxzoyTjTG5HppzxWjPT8JaF6Kz9TcRxv++h/wJJBnv/dVZ1WKG8AA80RkrYiMtsuCYXvpCBwDPre76j4RkfpBEnu5qgkJocoy1mFBhY77LWsbItIA+Bl4xBiTXJ51+6O0bRhjHMaYPlhH3P2BM8o7tvImIpcDR40xawMdSykNMsb0A0YA94vIEPeZVXh7qYXVtfuBMaYvkIbVfVPWekukMtooTk1ICAeA9m7v29llgXJERE4FsP89apcXFaev8nZeyn21USIiEoaVDL41xvwSbPEDGGMSgcVY3SBNRKSWl/ZcMdrzGwMnSvGZTvhowx/nA1eKSBzwHVa30VtBEDcAxpgD9r9HgV+xEnEwbC/xQLwxZpX9/iesBBEMsZermpAQ1gBd7VEU4VgX36YHMJ7pgHP0we1YffPO8tvsEQwDgCT7VHIucKmINLVHIFyK1b97CEgWkQH2iIXbCtTlrQ2/2XV+CmwzxkwKpvhFpKWINLGn62Jd+9iGlRiuLSJ2Z3vXAovso7XpwA1ijebpCHTFujjodZuy1ymqjWIZY8YaY9oZYyLsOhcZY26u6nEDiEh9EWnonMb6f95MEGwvxpjDwH4ROd0uuhjYGgyxl7tAXsCorBfWqIAdWP3I4yqx3anAISAH6yjkbqz+2oXATmAB0MxeVoD37Bg3AZFu9dwFxNqvO93KI7G+dLuAd8n/5bnXNkoY+yCs09eNwAb7NTIY4gd6Aevt2DcD4+3yTlg7xljgR6C2XV7Hfh9rz+/kVtc4O77t2CNDfG1TRbVRir//heSPMqrycdvrR9uvLc66g2F7sevoA0TZ28w0rFFCQRF7eb701hVKKaWAmtFlpJRSyg+aEJRSSgGaEJRSStk0ISillAI0ISillLJpQlDVilh3rbzPnm4jIj+VY92nisi88qqvBO3GiUiLym5X1TyaEFR10wTrLqAYYw4aY64tZvmSGI714yOlqiVNCKq6mQh0Fuue/D+K/SwKEblDRKbZ95yPE5EHROQx+2ZmK0Wkmb1cZxGZY9+gbZmIuN8DaTgw2z5T+MNuY7OIDLbX/UBEosTtGQx2eZyITLCXjxKRfiIyV0R2ici99jIX2nXOFOt5BR+KSKHvp4jcItazHjaIyGQRCa3Av6WqYTQhqOpmDLDLWDe2+3eBeT2Bq4FzgJeBdGPdzGwF1u0EwHpQ+oPGmLOBJ4D3wbp7KnC6MWYrcBPWLQn6AL2xfsUN1q9zI7F+KX2BiPRya3ufvfwy4ORWm0EAAAHmSURBVAus20QMAJ53W6Y/8CDW8ww627G6iMiZwD+A8+26HMDNJfrrKOVDreIXUaraWGyMSQFSRCQJ+N0u3wT0EuvOrucBP1q3nAGsh8yA9TwF583P1gCfiXXzv2nGGGdCuF6s2z7Xwrq3fXesWyFA/v2zNmE9vMcZR5bzvkvAamPMbgARmYp1+xD3ayAXA2cDa+z46hLgm6Gp6kUTgqpJstym89ze52F9F0KwngvQx8u6I4A5AMaYP8S6tfMo4AsRmYR15P8EcI4x5qSIfIF1r6GCbbu36942FL71ccH3AnxpjBnr60MqVVraZaSqmxSsR36WmLGe97BHRK4D17Nze9uzL8a6+Rgi0gE4Yoz5GPgE61bJjbDuo58kIq2xEkhJ9bfvRBqC1TW0vMD8hcC1ItLKjqOZHYtS5ULPEFS1Yow5ISJ/2heTt5WiipuBD0TkaaznMX8nIgeBTLubB6w7kf5bRHKAVOA2Y8weEVkPxGA9NevPUrS9ButOmF2wbkf9q/tMY8xWO655dtLIAe4H9paiLaUK0budKlUMEbkFaGeMmViBbVwIPGGMubyi2lCqOHqGoFQxjDHfBDoGpSqDniEopZQC9KKyUkopmyYEpZRSgCYEpZRSNk0ISimlAE0ISimlbP8PmkPr/Vnp3CIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wfdb.plot_wfdb(record)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Normal = [np.array(Image.open(i)) for i in glob('../data/2D/100/MLII/N/*.png')[:9]]\n",
    "A = [np.array(Image.open(i)) for i in glob('../data/2D/100/MLII/A/*.png')[:9]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=384x384 at 0x7F2022F9C990>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image.fromarray(np.vstack( [np.hstack(Normal[i::3]) for i in range(0,3)] ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=384x384 at 0x7F2022F97C90>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image.fromarray(np.vstack( [np.hstack(A[i::3]) for i in range(0,3)] ))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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